Artificial intelligence has already taken the step from experiments to productive applications in many companies. But with the growing use, the role of software is also changing. Instead of individual tools, systems are increasingly emerging that plan and execute tasks independently. This development is often described as Agentic AI.
AI has arrived in companies
Many organizations have already integrated AI into operational processes. The use ranges from analysis tools to automation and assistance systems. The NVIDIA State of AI Report, based on more than 3,200 company surveys, shows how far this development has already progressed.
Some key findings*:
- 76% of large companies are actively using AI
- 22% evaluate the use
- only 2% do not yet use AI
Economic effects are also visible. According to the report, 88% of companies say that AI has led to increased sales in parts of their business. These figures show one thing above all: AI is increasingly becoming part of the corporate infrastructure.
From AI tools to AI systems
Many organizations are starting their AI initiatives with individual applications. Often these are:
- Chatbots
- Copilot Assistant
- Automation of individual processes
Such applications deliver quickly visible results. At the same time, they often remain isolated solutions. However, as usage increases, another problem arises. Organizations need to coordinate multiple models, data sources, and workflows. This shifts the focus from individual tools to AI systems that are integrated into existing processes. This is where the transition to Agentic AI begins.
What Agentic AI Means
The term describes systems that not only generate answers, but can also perform multi-level tasks independently.
For example, an AI agent can:
- Research information
- Plan work steps
- Control different applications
- Merge results
This is increasingly resulting in software solutions consisting of several specialized agents. Kearney’s analyses describe this development as a new software architecture around agentic systems.
The emerging enterprise AI stack
With Agentic AI, a new technical structure is being created. Instead of individual applications, several levels work together.
A simplified architecture typically looks like this:
| Level | Function |
|---|---|
| Business Applications | Specialist applications and business processes |
| AI Agents | Autonomous agents for tasks and analyses |
| Orchestration Layer | Coordination of several agents |
| Governance & Security | Control, Policies, and Compliance |
| Data Platform | Data Platform and Models |
This structure shows that AI is increasingly being operated like a platform. The challenge lies less in individual models than in the integration and control of the entire system.
Why governance is becoming crucial
The more companies integrate AI into operational processes, the more important control mechanisms become. Agentic systems can prepare decisions or execute them automatically. This creates new requirements for control and transparency.
Typical questions are:
- Who monitors AI decisions?
- How are models checked?
- How do traceable audit trails come about?
These topics are often summarized under the term AI governance. Large technology providers are also increasingly integrating governance functions into their platforms. For example, Microsoft is expanding its Copilot platform to include capabilities to manage AI agents within enterprise environments.
Typical challenges with enterprise AI
Many organizations underestimate the structural requirements behind AI systems.
In practice, several hurdles often become apparent:
- Insufficient data quality
- Lack of architecture for AI workflows
- unclear governance structures
- siloed AI experiments without integration
Especially in larger organizations, this creates a fragmented system of tools and pilot projects. The transition to an integrated AI architecture therefore requires more than new models. Data structures, platform architecture and clear responsibilities are crucial.
Conclusion
AI is evolving from individual applications to integrated systems in many companies. Agentic AI is a new generation of software in which autonomous agents can plan and execute tasks. The actual competitive advantage is not created by individual AI tools. What matters is how well organizations build a stable architecture for data, models, and governance. Organizations that develop this structure early on create the foundation for scalable enterprise AI systems.
*Source: NVIDIA State of AI Report
**Source: Kearney The Age of Agents
